不確定環境下的交通建設BOT計畫權利金模式

Royalty Models for Transportation Infrastructure BOT Projects under Uncertain Environment

BOT計畫的不確定環境，主要來自運量預測之不確定性（資訊模糊）及參與者要求條件之認知模糊（本質模糊）。為處理此一不確定性問題，本文以政府立場為主，兼顧民間業者與融資者的基本財務要求限制下，構建一套BOT計畫之模糊型權利金模式，以計算各種不確定環境下之最佳權利金金額。同時探討不同權利金收取方式（一段式、兩段式、多段遞增式及多段遞減式）之權利金差異，以供研提最適權利金收取方式之參考。為驗證本模式之應用性，本文以一停車場BOT計畫為例，探討各種模糊情境之差異。結果顯示在不同情境下，皆以分年收取最適金額為最佳收取方式，二段式及多段遞增式次之。而相較於明確型權利金模式，在本質模糊情境下（投資者及融資者對財務條件之彈性要求），模糊型權利金模式所求得之分年權利金會有部分年期之權利金因而提高，但在資訊模糊情境下（運量預測之不確定性），則模糊型權利金模式計算所得之權利金則有降低現象。

There are two types of uncertainties in BOT projects: the imprecise prediction of transportation demand (information fuzziness) and the flexible requirements of related participants (intrinsic fuzziness). To incorporate the fuzziness, this paper employs fuzzy mathematical programming models to determine optimal annual royalty under various uncertain environments, based on the government's perspective and the requirements of the private institutions and financiers. Comparisons between different royalty collecting methods (uniform, two-staged, progressively increasing multi-staged and progressively decreasing multi-staged) are also conducted. To investigae the applicability of proposed models, a case study on a parking facility BOT project is conducted. The result shows that the annual optimal royalty scheme is the best royalty collection scheme, followed by progressively increasing multi-staged scheme. By comparing to the conventional deterministic royalty model, under the situation of intrinsic fuzziness, the annual royalty determined by fuzzy royalty model could be increased in some concession years; contrarily, under the situation of informational fuzziness, the annual royalty determined by fuzzy royalty model would be lowered in some concession years.